Enter the directory of the maca folder on your drive and the name of the tissue you want to analyze.

tissue_of_interest = "Tongue"

Load the requisite packages and some additional helper functions.

library(here)
library(useful)
library(Seurat)
library(dplyr)
library(Matrix)
library(ontologyIndex)
cell_ontology = get_ontology('https://raw.githubusercontent.com/obophenotype/cell-ontology/master/cl-basic.obo', extract_tags='everything')
validate_cell_ontology = function(cell_ontology_class){
  in_cell_ontology = sapply(cell_ontology_class, function(x) is.element(x, cell_ontology$name))
  if (!all(in_cell_ontology)) {
    message = paste0('"', cell_ontology_class[!in_cell_ontology], '" is not in the cell ontology
')
    stop(message)
  }
}
convert_to_cell_ontology_id = function(cell_ontology_class){
  return(sapply(cell_ontology_class, function(x) as.vector(cell_ontology$id[cell_ontology$name == x])[1]))
}
save_dir = here('00_data_ingest', 'tissue_robj')
# read the metadata to get the plates we want
plate_metadata_filename = here('00_data_ingest', '00_facs_raw_data', 'metadata_FACS.csv')
plate_metadata <- read.csv(plate_metadata_filename, sep=",", header = TRUE)
colnames(plate_metadata)[1] <- "plate.barcode"
plate_metadata

Subset the metadata on the tissue.

tissue_plates = filter(plate_metadata, tissue == tissue_of_interest)[,c('plate.barcode','tissue','subtissue','mouse.sex')]
tissue_plates

Load the read count data.

#Load the gene names and set the metadata columns by opening the first file
filename = here('00_data_ingest', '00_facs_raw_data', 'FACS', paste0(tissue_of_interest, '-counts.csv'))
raw.data = read.csv(filename, sep=",", row.names=1)
# raw.data = data.frame(row.names = rownames(raw.data))
corner(raw.data)

Make a vector of plate barcodes for each cell

plate.barcodes = lapply(colnames(raw.data), function(x) strsplit(strsplit(x, "_")[[1]][1], '.', fixed=TRUE)[[1]][2])
head(plate.barcodes)
[[1]]
[1] "D041894"

[[2]]
[1] "D041894"

[[3]]
[1] "D041894"

[[4]]
[1] "D041894"

[[5]]
[1] "D041894"

[[6]]
[1] "D041894"

Use only the metadata rows corresponding to Bladder plates. Make a plate barcode dataframe to “expand” the per-plate metadata to be per-cell.

barcode.df = t.data.frame(as.data.frame(plate.barcodes))
rownames(barcode.df) = colnames(raw.data)
colnames(barcode.df) = c('plate.barcode')
head(barcode.df)
                      plate.barcode
A15.D041894.3_9_M.1.1 "D041894"    
B19.D041894.3_9_M.1.1 "D041894"    
C21.D041894.3_9_M.1.1 "D041894"    
E1.D041894.3_9_M.1.1  "D041894"    
F4.D041894.3_9_M.1.1  "D041894"    
G7.D041894.3_9_M.1.1  "D041894"    
rnames = row.names(barcode.df)
meta.data <- merge(barcode.df, plate_metadata, by='plate.barcode', sort = F)
row.names(meta.data) <- rnames
# Sort cells by plate barcode because that's how the data was originally
meta.data = meta.data[order(meta.data$plate.barcode), ]
corner(meta.data)
raw.data = raw.data[, rownames(meta.data)]
corner(raw.data)

Process the raw data and load it into the Seurat object.

# Find ERCC's, compute the percent ERCC, and drop them from the raw data.
erccs <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = TRUE)
percent.ercc <- Matrix::colSums(raw.data[erccs, ])/Matrix::colSums(raw.data)
ercc.index <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = FALSE)
raw.data <- raw.data[-ercc.index,]
# Create the Seurat object with all the data
tiss <- CreateSeuratObject(raw.data = raw.data, project = tissue_of_interest, 
                    min.cells = 5, min.genes = 5)
tiss <- AddMetaData(object = tiss, meta.data)
tiss <- AddMetaData(object = tiss, percent.ercc, col.name = "percent.ercc")
# Change default name for sums of counts from nUMI to nReads
colnames(tiss@meta.data)[colnames(tiss@meta.data) == 'nUMI'] <- 'nReads'
# Create metadata columns for cell_ontology_classs and subcell_ontology_classs
tiss@meta.data[,'cell_ontology_class'] <- NA
tiss@meta.data[,'subcell_ontology_class'] <- NA

Calculate percent ribosomal genes.

ribo.genes <- grep(pattern = "^Rp[sl][[:digit:]]", x = rownames(x = tiss@data), value = TRUE)
percent.ribo <- Matrix::colSums(tiss@raw.data[ribo.genes, ])/Matrix::colSums(tiss@raw.data)
tiss <- AddMetaData(object = tiss, metadata = percent.ribo, col.name = "percent.ribo")

A sanity check: genes per cell vs reads per cell.

GenePlot(object = tiss, gene1 = "nReads", gene2 = "nGene", use.raw=T)

Filter out cells with few reads and few genes.

tiss <- FilterCells(object = tiss, subset.names = c("nGene", "nReads"), 
    low.thresholds = c(500, 50000), high.thresholds = c(25000, 2000000))

Normalize the data, then regress out correlation with total reads

tiss <- NormalizeData(object = tiss, scale.factor = 1e6)
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|
tiss <- ScaleData(object = tiss)
[1] "Scaling data matrix"

  |                                                                          
  |                                                                    |   0%
  |                                                                          
  |====================================================================| 100%
tiss <- FindVariableGenes(object = tiss, do.plot = TRUE, x.high.cutoff = Inf, y.cutoff = 0.5)
Calculating gene means
0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|

Run Principal Component Analysis.

tiss <- RunPCA(object = tiss, do.print = FALSE)
tiss <- ProjectPCA(object = tiss, do.print = FALSE)

Later on (in FindClusters and TSNE) you will pick a number of principal components to use. This has the effect of keeping the major directions of variation in the data and, ideally, supressing noise. There is no correct answer to the number to use, but a decent rule of thumb is to go until the plot plateaus.

PCElbowPlot(object = tiss)

Choose the number of principal components to use.

# Set number of principal components. 
n.pcs = 10

The clustering is performed based on a nearest neighbors graph. Cells that have similar expression will be joined together. The Louvain algorithm looks for groups of cells with high modularity–more connections within the group than between groups. The resolution parameter determines the scale…higher resolution will give more clusters, lower resolution will give fewer.

For the top-level clustering, aim to under-cluster instead of over-cluster. It will be easy to subset groups and further analyze them below.

# Set resolution 
res.used <- 0.5
tiss <- FindClusters(object = tiss, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)

To visualize

# If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10).
tiss <- RunTSNE(object = tiss, dims.use = 1:n.pcs, seed.use = 10, perplexity=30)
# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = tiss, do.label = T, pt.size = 1.2, label.size = 4)

Check expression of genes of interset.

Dotplots let you see the intensity of exppression and the fraction of cells expressing for each of your genes of interest.

How big are the clusters?

table(tiss@ident)

  0   1   2   3   4   5 
461 274 194 178 163 124 
tiss1=BuildClusterTree(tiss)
[1] "Finished averaging RNA for cluster 0"
[1] "Finished averaging RNA for cluster 1"
[1] "Finished averaging RNA for cluster 2"
[1] "Finished averaging RNA for cluster 3"
[1] "Finished averaging RNA for cluster 4"
[1] "Finished averaging RNA for cluster 5"

marker10=FindAllMarkersNode(tiss1, 10)

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~20s          
   |++                                                | 2 % ~18s          
   |++                                                | 3 % ~18s          
   |+++                                               | 4 % ~18s          
   |+++                                               | 5 % ~18s          
   |++++                                              | 7 % ~18s          
   |++++                                              | 8 % ~18s          
   |+++++                                             | 9 % ~17s          
   |+++++                                             | 10% ~17s          
   |++++++                                            | 11% ~17s          
   |+++++++                                           | 12% ~17s          
   |+++++++                                           | 13% ~17s          
   |++++++++                                          | 14% ~16s          
   |++++++++                                          | 15% ~16s          
   |+++++++++                                         | 16% ~16s          
   |+++++++++                                         | 18% ~16s          
   |++++++++++                                        | 19% ~15s          
   |++++++++++                                        | 20% ~15s          
   |+++++++++++                                       | 21% ~15s          
   |+++++++++++                                       | 22% ~15s          
   |++++++++++++                                      | 23% ~15s          
   |+++++++++++++                                     | 24% ~15s          
   |+++++++++++++                                     | 25% ~15s          
   |++++++++++++++                                    | 26% ~14s          
   |++++++++++++++                                    | 27% ~14s          
   |+++++++++++++++                                   | 29% ~14s          
   |+++++++++++++++                                   | 30% ~14s          
   |++++++++++++++++                                  | 31% ~13s          
   |++++++++++++++++                                  | 32% ~13s          
   |+++++++++++++++++                                 | 33% ~13s          
   |++++++++++++++++++                                | 34% ~13s          
   |++++++++++++++++++                                | 35% ~12s          
   |+++++++++++++++++++                               | 36% ~12s          
   |+++++++++++++++++++                               | 37% ~12s          
   |++++++++++++++++++++                              | 38% ~12s          
   |++++++++++++++++++++                              | 40% ~11s          
   |+++++++++++++++++++++                             | 41% ~11s          
   |+++++++++++++++++++++                             | 42% ~11s          
   |++++++++++++++++++++++                            | 43% ~11s          
   |++++++++++++++++++++++                            | 44% ~11s          
   |+++++++++++++++++++++++                           | 45% ~10s          
   |++++++++++++++++++++++++                          | 46% ~10s          
   |++++++++++++++++++++++++                          | 47% ~10s          
   |+++++++++++++++++++++++++                         | 48% ~10s          
   |+++++++++++++++++++++++++                         | 49% ~09s          
   |++++++++++++++++++++++++++                        | 51% ~09s          
   |++++++++++++++++++++++++++                        | 52% ~09s          
   |+++++++++++++++++++++++++++                       | 53% ~09s          
   |+++++++++++++++++++++++++++                       | 54% ~09s          
   |++++++++++++++++++++++++++++                      | 55% ~08s          
   |+++++++++++++++++++++++++++++                     | 56% ~08s          
   |+++++++++++++++++++++++++++++                     | 57% ~08s          
   |++++++++++++++++++++++++++++++                    | 58% ~08s          
   |++++++++++++++++++++++++++++++                    | 59% ~08s          
   |+++++++++++++++++++++++++++++++                   | 60% ~07s          
   |+++++++++++++++++++++++++++++++                   | 62% ~07s          
   |++++++++++++++++++++++++++++++++                  | 63% ~07s          
   |++++++++++++++++++++++++++++++++                  | 64% ~07s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~07s          
   |+++++++++++++++++++++++++++++++++                 | 66% ~06s          
   |++++++++++++++++++++++++++++++++++                | 67% ~06s          
   |+++++++++++++++++++++++++++++++++++               | 68% ~06s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~06s          
   |++++++++++++++++++++++++++++++++++++              | 70% ~06s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~05s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~05s          
   |+++++++++++++++++++++++++++++++++++++             | 74% ~05s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~05s          
   |++++++++++++++++++++++++++++++++++++++            | 76% ~05s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~04s          
   |++++++++++++++++++++++++++++++++++++++++          | 78% ~04s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~04s          
   |+++++++++++++++++++++++++++++++++++++++++         | 80% ~04s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~04s          
   |++++++++++++++++++++++++++++++++++++++++++        | 82% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~03s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~03s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 19s

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~13s          
   |++                                                | 2 % ~11s          
   |++                                                | 4 % ~11s          
   |+++                                               | 5 % ~10s          
   |++++                                              | 6 % ~10s          
   |++++                                              | 7 % ~10s          
   |+++++                                             | 8 % ~10s          
   |+++++                                             | 10% ~09s          
   |++++++                                            | 11% ~10s          
   |+++++++                                           | 12% ~09s          
   |+++++++                                           | 13% ~09s          
   |++++++++                                          | 14% ~09s          
   |++++++++                                          | 16% ~09s          
   |+++++++++                                         | 17% ~09s          
   |++++++++++                                        | 18% ~08s          
   |++++++++++                                        | 19% ~08s          
   |+++++++++++                                       | 20% ~08s          
   |+++++++++++                                       | 22% ~08s          
   |++++++++++++                                      | 23% ~08s          
   |+++++++++++++                                     | 24% ~08s          
   |+++++++++++++                                     | 25% ~08s          
   |++++++++++++++                                    | 27% ~08s          
   |++++++++++++++                                    | 28% ~07s          
   |+++++++++++++++                                   | 29% ~07s          
   |++++++++++++++++                                  | 30% ~07s          
   |++++++++++++++++                                  | 31% ~07s          
   |+++++++++++++++++                                 | 33% ~07s          
   |+++++++++++++++++                                 | 34% ~07s          
   |++++++++++++++++++                                | 35% ~07s          
   |+++++++++++++++++++                               | 36% ~07s          
   |+++++++++++++++++++                               | 37% ~06s          
   |++++++++++++++++++++                              | 39% ~06s          
   |++++++++++++++++++++                              | 40% ~06s          
   |+++++++++++++++++++++                             | 41% ~06s          
   |++++++++++++++++++++++                            | 42% ~06s          
   |++++++++++++++++++++++                            | 43% ~06s          
   |+++++++++++++++++++++++                           | 45% ~06s          
   |+++++++++++++++++++++++                           | 46% ~06s          
   |++++++++++++++++++++++++                          | 47% ~05s          
   |+++++++++++++++++++++++++                         | 48% ~05s          
   |+++++++++++++++++++++++++                         | 49% ~05s          
   |++++++++++++++++++++++++++                        | 51% ~05s          
   |++++++++++++++++++++++++++                        | 52% ~05s          
   |+++++++++++++++++++++++++++                       | 53% ~05s          
   |++++++++++++++++++++++++++++                      | 54% ~05s          
   |++++++++++++++++++++++++++++                      | 55% ~05s          
   |+++++++++++++++++++++++++++++                     | 57% ~04s          
   |+++++++++++++++++++++++++++++                     | 58% ~04s          
   |++++++++++++++++++++++++++++++                    | 59% ~04s          
   |+++++++++++++++++++++++++++++++                   | 60% ~04s          
   |+++++++++++++++++++++++++++++++                   | 61% ~04s          
   |++++++++++++++++++++++++++++++++                  | 63% ~04s          
   |++++++++++++++++++++++++++++++++                  | 64% ~04s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~04s          
   |++++++++++++++++++++++++++++++++++                | 66% ~04s          
   |++++++++++++++++++++++++++++++++++                | 67% ~03s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~03s          
   |+++++++++++++++++++++++++++++++++++               | 70% ~03s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~03s          
   |+++++++++++++++++++++++++++++++++++++             | 72% ~03s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~03s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~03s          
   |++++++++++++++++++++++++++++++++++++++            | 76% ~03s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~02s          
   |++++++++++++++++++++++++++++++++++++++++          | 78% ~02s          
   |++++++++++++++++++++++++++++++++++++++++          | 80% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 10s
print(x = head(x= marker10, n = 30))

Which markers identify a specific cluster?

clust.markers <- FindMarkers(object = tiss, ident.1 = 2, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~26s          
   |++                                                | 2 % ~27s          
   |++                                                | 3 % ~26s          
   |+++                                               | 4 % ~25s          
   |+++                                               | 5 % ~25s          
   |++++                                              | 6 % ~24s          
   |++++                                              | 7 % ~24s          
   |+++++                                             | 8 % ~24s          
   |+++++                                             | 9 % ~23s          
   |++++++                                            | 11% ~23s          
   |++++++                                            | 12% ~23s          
   |+++++++                                           | 13% ~23s          
   |+++++++                                           | 14% ~22s          
   |++++++++                                          | 15% ~22s          
   |++++++++                                          | 16% ~22s          
   |+++++++++                                         | 17% ~22s          
   |+++++++++                                         | 18% ~21s          
   |++++++++++                                        | 19% ~21s          
   |++++++++++                                        | 20% ~21s          
   |+++++++++++                                       | 21% ~21s          
   |++++++++++++                                      | 22% ~21s          
   |++++++++++++                                      | 23% ~20s          
   |+++++++++++++                                     | 24% ~20s          
   |+++++++++++++                                     | 25% ~20s          
   |++++++++++++++                                    | 26% ~19s          
   |++++++++++++++                                    | 27% ~19s          
   |+++++++++++++++                                   | 28% ~19s          
   |+++++++++++++++                                   | 29% ~19s          
   |++++++++++++++++                                  | 31% ~18s          
   |++++++++++++++++                                  | 32% ~18s          
   |+++++++++++++++++                                 | 33% ~18s          
   |+++++++++++++++++                                 | 34% ~18s          
   |++++++++++++++++++                                | 35% ~17s          
   |++++++++++++++++++                                | 36% ~17s          
   |+++++++++++++++++++                               | 37% ~17s          
   |+++++++++++++++++++                               | 38% ~16s          
   |++++++++++++++++++++                              | 39% ~16s          
   |++++++++++++++++++++                              | 40% ~16s          
   |+++++++++++++++++++++                             | 41% ~16s          
   |++++++++++++++++++++++                            | 42% ~15s          
   |++++++++++++++++++++++                            | 43% ~15s          
   |+++++++++++++++++++++++                           | 44% ~15s          
   |+++++++++++++++++++++++                           | 45% ~15s          
   |++++++++++++++++++++++++                          | 46% ~15s          
   |++++++++++++++++++++++++                          | 47% ~14s          
   |+++++++++++++++++++++++++                         | 48% ~14s          
   |+++++++++++++++++++++++++                         | 49% ~14s          
   |++++++++++++++++++++++++++                        | 51% ~14s          
   |++++++++++++++++++++++++++                        | 52% ~14s          
   |+++++++++++++++++++++++++++                       | 53% ~13s          
   |+++++++++++++++++++++++++++                       | 54% ~13s          
   |++++++++++++++++++++++++++++                      | 55% ~13s          
   |++++++++++++++++++++++++++++                      | 56% ~13s          
   |+++++++++++++++++++++++++++++                     | 57% ~12s          
   |+++++++++++++++++++++++++++++                     | 58% ~12s          
   |++++++++++++++++++++++++++++++                    | 59% ~12s          
   |++++++++++++++++++++++++++++++                    | 60% ~11s          
   |+++++++++++++++++++++++++++++++                   | 61% ~11s          
   |++++++++++++++++++++++++++++++++                  | 62% ~11s          
   |++++++++++++++++++++++++++++++++                  | 63% ~11s          
   |+++++++++++++++++++++++++++++++++                 | 64% ~10s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~10s          
   |++++++++++++++++++++++++++++++++++                | 66% ~10s          
   |++++++++++++++++++++++++++++++++++                | 67% ~09s          
   |+++++++++++++++++++++++++++++++++++               | 68% ~09s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~09s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~08s          
   |++++++++++++++++++++++++++++++++++++              | 72% ~08s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~08s          
   |+++++++++++++++++++++++++++++++++++++             | 74% ~07s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~07s          
   |++++++++++++++++++++++++++++++++++++++            | 76% ~07s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~07s          
   |+++++++++++++++++++++++++++++++++++++++           | 78% ~06s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~06s          
   |++++++++++++++++++++++++++++++++++++++++         | 80% ~06s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~05s          
   |++++++++++++++++++++++++++++++++++++++++++        | 82% ~05s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~05s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~04s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~04s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~04s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~04s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~03s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 28s
print(x = head(x= clust.markers, n = 30))

You can also compute all markers for all clusters at once. This may take some time.

tiss.markers <- FindAllMarkers(object = tiss, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~58s          
   |++                                                | 2 % ~53s          
   |++                                                | 3 % ~52s          
   |+++                                               | 4 % ~50s          
   |+++                                               | 5 % ~49s          
   |++++                                              | 6 % ~49s          
   |++++                                              | 7 % ~48s          
   |+++++                                             | 8 % ~48s          
   |+++++                                             | 9 % ~47s          
   |++++++                                            | 10% ~47s          
   |++++++                                            | 11% ~46s          
   |+++++++                                           | 12% ~46s          
   |+++++++                                           | 13% ~46s          
   |++++++++                                          | 14% ~45s          
   |++++++++                                          | 15% ~45s          
   |+++++++++                                         | 16% ~44s          
   |+++++++++                                         | 17% ~44s          
   |++++++++++                                        | 18% ~43s          
   |++++++++++                                        | 19% ~42s          
   |+++++++++++                                       | 20% ~42s          
   |+++++++++++                                       | 21% ~42s          
   |++++++++++++                                      | 22% ~41s          
   |++++++++++++                                      | 23% ~40s          
   |+++++++++++++                                     | 24% ~40s          
   |+++++++++++++                                     | 25% ~39s          
   |++++++++++++++                                    | 26% ~39s          
   |++++++++++++++                                    | 27% ~38s          
   |+++++++++++++++                                   | 28% ~38s          
   |+++++++++++++++                                   | 29% ~37s          
   |++++++++++++++++                                  | 30% ~36s          
   |++++++++++++++++                                  | 31% ~36s          
   |+++++++++++++++++                                 | 32% ~35s          
   |+++++++++++++++++                                 | 33% ~35s          
   |++++++++++++++++++                                | 34% ~34s          
   |++++++++++++++++++                                | 35% ~34s          
   |+++++++++++++++++++                               | 36% ~33s          
   |+++++++++++++++++++                               | 37% ~33s          
   |++++++++++++++++++++                              | 38% ~32s          
   |++++++++++++++++++++                              | 39% ~32s          
   |+++++++++++++++++++++                             | 40% ~31s          
   |+++++++++++++++++++++                             | 41% ~31s          
   |++++++++++++++++++++++                            | 42% ~30s          
   |++++++++++++++++++++++                            | 43% ~30s          
   |+++++++++++++++++++++++                           | 44% ~29s          
   |+++++++++++++++++++++++                           | 45% ~28s          
   |++++++++++++++++++++++++                          | 46% ~28s          
   |++++++++++++++++++++++++                          | 47% ~27s          
   |+++++++++++++++++++++++++                         | 48% ~27s          
   |+++++++++++++++++++++++++                         | 49% ~26s          
   |++++++++++++++++++++++++++                        | 51% ~26s          
   |++++++++++++++++++++++++++                        | 52% ~25s          
   |+++++++++++++++++++++++++++                       | 53% ~25s          
   |+++++++++++++++++++++++++++                       | 54% ~24s          
   |++++++++++++++++++++++++++++                      | 55% ~24s          
   |++++++++++++++++++++++++++++                      | 56% ~23s          
   |+++++++++++++++++++++++++++++                     | 57% ~23s          
   |+++++++++++++++++++++++++++++                     | 58% ~22s          
   |++++++++++++++++++++++++++++++                    | 59% ~22s          
   |++++++++++++++++++++++++++++++                    | 60% ~21s          
   |+++++++++++++++++++++++++++++++                   | 61% ~21s          
   |+++++++++++++++++++++++++++++++                   | 62% ~20s          
   |++++++++++++++++++++++++++++++++                  | 63% ~20s          
   |++++++++++++++++++++++++++++++++                  | 64% ~19s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~19s          
   |+++++++++++++++++++++++++++++++++                 | 66% ~18s          
   |++++++++++++++++++++++++++++++++++                | 67% ~18s          
   |++++++++++++++++++++++++++++++++++                | 68% ~17s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~16s          
   |+++++++++++++++++++++++++++++++++++               | 70% ~16s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~16s          
   |++++++++++++++++++++++++++++++++++++              | 72% ~15s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~15s          
   |+++++++++++++++++++++++++++++++++++++             | 74% ~14s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~14s          
   |++++++++++++++++++++++++++++++++++++++            | 76% ~13s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~13s          
   |+++++++++++++++++++++++++++++++++++++++           | 78% ~12s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~11s          
   |++++++++++++++++++++++++++++++++++++++++          | 80% ~11s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~10s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~10s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~09s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~09s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~08s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~08s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~07s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~07s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~06s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~05s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~05s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~04s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~04s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 54s

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~01m 04s      
   |++                                                | 2 % ~01m 00s      
   |++                                                | 3 % ~59s          
   |+++                                               | 4 % ~57s          
   |+++                                               | 5 % ~57s          
   |++++                                              | 6 % ~56s          
   |++++                                              | 7 % ~56s          
   |+++++                                             | 8 % ~55s          
   |+++++                                             | 9 % ~54s          
   |++++++                                            | 10% ~54s          
   |++++++                                            | 11% ~53s          
   |+++++++                                           | 12% ~53s          
   |+++++++                                           | 13% ~53s          
   |++++++++                                          | 14% ~52s          
   |++++++++                                          | 15% ~51s          
   |+++++++++                                         | 16% ~51s          
   |+++++++++                                         | 17% ~50s          
   |++++++++++                                        | 18% ~50s          
   |++++++++++                                        | 19% ~49s          
   |+++++++++++                                       | 20% ~48s          
   |+++++++++++                                       | 21% ~48s          
   |++++++++++++                                      | 22% ~47s          
   |++++++++++++                                      | 23% ~46s          
   |+++++++++++++                                     | 24% ~45s          
   |+++++++++++++                                     | 26% ~45s          
   |++++++++++++++                                    | 27% ~44s          
   |++++++++++++++                                    | 28% ~43s          
   |+++++++++++++++                                   | 29% ~43s          
   |+++++++++++++++                                   | 30% ~42s          
   |++++++++++++++++                                  | 31% ~42s          
   |++++++++++++++++                                  | 32% ~41s          
   |+++++++++++++++++                                 | 33% ~40s          
   |+++++++++++++++++                                 | 34% ~40s          
   |++++++++++++++++++                                | 35% ~39s          
   |++++++++++++++++++                                | 36% ~38s          
   |+++++++++++++++++++                               | 37% ~38s          
   |+++++++++++++++++++                               | 38% ~37s          
   |++++++++++++++++++++                              | 39% ~37s          
   |++++++++++++++++++++                              | 40% ~36s          
   |+++++++++++++++++++++                             | 41% ~35s          
   |+++++++++++++++++++++                             | 42% ~35s          
   |++++++++++++++++++++++                            | 43% ~34s          
   |++++++++++++++++++++++                            | 44% ~34s          
   |+++++++++++++++++++++++                           | 45% ~33s          
   |+++++++++++++++++++++++                           | 46% ~32s          
   |++++++++++++++++++++++++                          | 47% ~32s          
   |++++++++++++++++++++++++                          | 48% ~31s          
   |+++++++++++++++++++++++++                         | 49% ~31s          
   |+++++++++++++++++++++++++                         | 50% ~30s          
   |++++++++++++++++++++++++++                        | 51% ~29s          
   |+++++++++++++++++++++++++++                       | 52% ~29s          
   |+++++++++++++++++++++++++++                       | 53% ~28s          
   |++++++++++++++++++++++++++++                      | 54% ~27s          
   |++++++++++++++++++++++++++++                      | 55% ~27s          
   |+++++++++++++++++++++++++++++                     | 56% ~26s          
   |+++++++++++++++++++++++++++++                     | 57% ~26s          
   |++++++++++++++++++++++++++++++                    | 58% ~25s          
   |++++++++++++++++++++++++++++++                    | 59% ~24s          
   |+++++++++++++++++++++++++++++++                   | 60% ~24s          
   |+++++++++++++++++++++++++++++++                   | 61% ~23s          
   |++++++++++++++++++++++++++++++++                  | 62% ~23s          
   |++++++++++++++++++++++++++++++++                  | 63% ~22s          
   |+++++++++++++++++++++++++++++++++                 | 64% ~21s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~21s          
   |++++++++++++++++++++++++++++++++++                | 66% ~20s          
   |++++++++++++++++++++++++++++++++++                | 67% ~20s          
   |+++++++++++++++++++++++++++++++++++               | 68% ~19s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~18s          
   |++++++++++++++++++++++++++++++++++++              | 70% ~18s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~17s          
   |+++++++++++++++++++++++++++++++++++++             | 72% ~17s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~16s          
   |++++++++++++++++++++++++++++++++++++++            | 74% ~15s          
   |++++++++++++++++++++++++++++++++++++++            | 76% ~15s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~14s          
   |+++++++++++++++++++++++++++++++++++++++           | 78% ~13s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~13s          
   |++++++++++++++++++++++++++++++++++++++++          | 80% ~12s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~12s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~11s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~10s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~10s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~09s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~09s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~08s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~07s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~07s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~06s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~06s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~05s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~04s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~04s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 59s

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~01m 33s      
   |++                                                | 2 % ~01m 29s      
   |++                                                | 3 % ~01m 27s      
   |+++                                               | 4 % ~01m 26s      
   |+++                                               | 5 % ~01m 26s      
   |++++                                              | 6 % ~01m 24s      
   |++++                                              | 7 % ~01m 23s      
   |+++++                                             | 8 % ~01m 22s      
   |+++++                                             | 9 % ~01m 21s      
   |++++++                                            | 10% ~01m 20s      
   |++++++                                            | 11% ~01m 19s      
   |+++++++                                           | 12% ~01m 20s      
   |+++++++                                           | 13% ~01m 21s      
   |++++++++                                          | 14% ~01m 21s      
   |++++++++                                          | 15% ~01m 21s      
   |+++++++++                                         | 16% ~01m 19s      
   |+++++++++                                         | 17% ~01m 18s      
   |++++++++++                                        | 18% ~01m 17s      
   |++++++++++                                        | 19% ~01m 16s      
   |+++++++++++                                       | 20% ~01m 15s      
   |+++++++++++                                       | 21% ~01m 14s      
   |++++++++++++                                      | 22% ~01m 13s      
   |++++++++++++                                      | 23% ~01m 12s      
   |+++++++++++++                                     | 24% ~01m 11s      
   |+++++++++++++                                     | 25% ~01m 10s      
   |++++++++++++++                                    | 26% ~01m 08s      
   |++++++++++++++                                    | 27% ~01m 07s      
   |+++++++++++++++                                   | 28% ~01m 06s      
   |+++++++++++++++                                   | 29% ~01m 06s      
   |++++++++++++++++                                  | 30% ~01m 05s      
   |++++++++++++++++                                  | 31% ~01m 04s      
   |+++++++++++++++++                                 | 32% ~01m 03s      
   |+++++++++++++++++                                 | 33% ~01m 02s      
   |++++++++++++++++++                                | 34% ~01m 01s      
   |++++++++++++++++++                                | 35% ~01m 00s      
   |+++++++++++++++++++                               | 36% ~59s          
   |+++++++++++++++++++                               | 37% ~58s          
   |++++++++++++++++++++                              | 38% ~57s          
   |++++++++++++++++++++                              | 39% ~56s          
   |+++++++++++++++++++++                             | 40% ~55s          
   |+++++++++++++++++++++                             | 41% ~54s          
   |++++++++++++++++++++++                            | 42% ~53s          
   |++++++++++++++++++++++                            | 43% ~52s          
   |+++++++++++++++++++++++                           | 44% ~52s          
   |+++++++++++++++++++++++                           | 45% ~51s          
   |++++++++++++++++++++++++                          | 46% ~50s          
   |++++++++++++++++++++++++                          | 47% ~49s          
   |+++++++++++++++++++++++++                         | 48% ~48s          
   |+++++++++++++++++++++++++                         | 49% ~47s          
   |++++++++++++++++++++++++++                        | 51% ~46s          
   |++++++++++++++++++++++++++                        | 52% ~45s          
   |+++++++++++++++++++++++++++                       | 53% ~44s          
   |+++++++++++++++++++++++++++                       | 54% ~43s          
   |++++++++++++++++++++++++++++                      | 55% ~42s          
   |++++++++++++++++++++++++++++                      | 56% ~41s          
   |+++++++++++++++++++++++++++++                     | 57% ~40s          
   |+++++++++++++++++++++++++++++                     | 58% ~39s          
   |++++++++++++++++++++++++++++++                    | 59% ~38s          
   |++++++++++++++++++++++++++++++                    | 60% ~37s          
   |+++++++++++++++++++++++++++++++                   | 61% ~36s          
   |+++++++++++++++++++++++++++++++                   | 62% ~35s          
   |++++++++++++++++++++++++++++++++                  | 63% ~34s          
   |++++++++++++++++++++++++++++++++                  | 64% ~34s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~33s          
   |+++++++++++++++++++++++++++++++++                 | 66% ~32s          
   |++++++++++++++++++++++++++++++++++                | 67% ~31s          
   |++++++++++++++++++++++++++++++++++                | 68% ~30s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~29s          
   |+++++++++++++++++++++++++++++++++++               | 70% ~28s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~27s          
   |++++++++++++++++++++++++++++++++++++              | 72% ~26s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~25s          
   |+++++++++++++++++++++++++++++++++++++             | 74% ~24s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~23s          
   |++++++++++++++++++++++++++++++++++++++            | 76% ~22s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~22s          
   |+++++++++++++++++++++++++++++++++++++++           | 78% ~21s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~20s          
   |++++++++++++++++++++++++++++++++++++++++          | 80% ~19s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~18s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~17s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~16s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~15s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~14s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~13s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~12s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~11s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~10s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~09s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~08s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~07s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~07s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~06s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~05s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~04s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 32s

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~01m 20s      
   |++                                                | 2 % ~01m 12s      
   |++                                                | 3 % ~01m 10s      
   |+++                                               | 4 % ~01m 07s      
   |+++                                               | 5 % ~01m 06s      
   |++++                                              | 6 % ~01m 06s      
   |++++                                              | 7 % ~01m 04s      
   |+++++                                             | 8 % ~01m 04s      
   |+++++                                             | 9 % ~01m 03s      
   |++++++                                            | 10% ~01m 02s      
   |++++++                                            | 11% ~01m 01s      
   |+++++++                                           | 12% ~01m 01s      
   |+++++++                                           | 13% ~01m 02s      
   |++++++++                                          | 14% ~01m 04s      
   |++++++++                                          | 15% ~01m 08s      
   |+++++++++                                         | 16% ~01m 08s      
   |+++++++++                                         | 17% ~01m 07s      
   |++++++++++                                        | 18% ~01m 05s      
   |++++++++++                                        | 19% ~01m 04s      
   |+++++++++++                                       | 20% ~01m 02s      
   |+++++++++++                                       | 21% ~01m 01s      
   |++++++++++++                                      | 22% ~01m 00s      
   |++++++++++++                                      | 23% ~59s          
   |+++++++++++++                                     | 24% ~58s          
   |+++++++++++++                                     | 25% ~57s          
   |++++++++++++++                                    | 26% ~56s          
   |++++++++++++++                                    | 27% ~55s          
   |+++++++++++++++                                   | 28% ~54s          
   |+++++++++++++++                                   | 29% ~53s          
   |++++++++++++++++                                  | 30% ~52s          
   |++++++++++++++++                                  | 31% ~51s          
   |+++++++++++++++++                                 | 32% ~50s          
   |+++++++++++++++++                                 | 33% ~49s          
   |++++++++++++++++++                                | 34% ~48s          
   |++++++++++++++++++                                | 35% ~47s          
   |+++++++++++++++++++                               | 36% ~47s          
   |+++++++++++++++++++                               | 37% ~46s          
   |++++++++++++++++++++                              | 38% ~45s          
   |++++++++++++++++++++                              | 39% ~44s          
   |+++++++++++++++++++++                             | 40% ~43s          
   |+++++++++++++++++++++                             | 41% ~42s          
   |++++++++++++++++++++++                            | 42% ~41s          
   |++++++++++++++++++++++                            | 43% ~41s          
   |+++++++++++++++++++++++                           | 44% ~40s          
   |+++++++++++++++++++++++                           | 45% ~39s          
   |++++++++++++++++++++++++                          | 46% ~38s          
   |++++++++++++++++++++++++                          | 47% ~38s          
   |+++++++++++++++++++++++++                         | 48% ~37s          
   |+++++++++++++++++++++++++                         | 49% ~36s          
   |++++++++++++++++++++++++++                        | 51% ~35s          
   |++++++++++++++++++++++++++                        | 52% ~34s          
   |+++++++++++++++++++++++++++                       | 53% ~34s          
   |+++++++++++++++++++++++++++                       | 54% ~33s          
   |++++++++++++++++++++++++++++                      | 55% ~32s          
   |++++++++++++++++++++++++++++                      | 56% ~31s          
   |+++++++++++++++++++++++++++++                     | 57% ~31s          
   |+++++++++++++++++++++++++++++                     | 58% ~30s          
   |++++++++++++++++++++++++++++++                    | 59% ~29s          
   |++++++++++++++++++++++++++++++                    | 60% ~28s          
   |+++++++++++++++++++++++++++++++                   | 61% ~28s          
   |+++++++++++++++++++++++++++++++                   | 62% ~27s          
   |++++++++++++++++++++++++++++++++                  | 63% ~26s          
   |++++++++++++++++++++++++++++++++                  | 64% ~25s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~25s          
   |+++++++++++++++++++++++++++++++++                 | 66% ~24s          
   |++++++++++++++++++++++++++++++++++                | 67% ~23s          
   |++++++++++++++++++++++++++++++++++                | 68% ~23s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~22s          
   |+++++++++++++++++++++++++++++++++++               | 70% ~21s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~20s          
   |++++++++++++++++++++++++++++++++++++              | 72% ~20s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~19s          
   |+++++++++++++++++++++++++++++++++++++             | 74% ~18s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~18s          
   |++++++++++++++++++++++++++++++++++++++            | 76% ~17s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~16s          
   |+++++++++++++++++++++++++++++++++++++++           | 78% ~15s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~15s          
   |++++++++++++++++++++++++++++++++++++++++          | 80% ~14s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~13s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~13s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~12s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~11s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~11s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~10s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~09s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~09s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~08s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~07s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~06s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~06s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~05s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~04s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~04s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~03s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 10s

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~57s          
   |+                                                 | 2 % ~58s          
   |++                                                | 3 % ~01m 00s      
   |++                                                | 4 % ~01m 02s      
   |+++                                               | 5 % ~60s          
   |+++                                               | 6 % ~58s          
   |++++                                              | 7 % ~57s          
   |++++                                              | 8 % ~56s          
   |+++++                                             | 9 % ~56s          
   |+++++                                             | 10% ~55s          
   |++++++                                            | 11% ~54s          
   |++++++                                            | 12% ~53s          
   |+++++++                                           | 13% ~52s          
   |+++++++                                           | 14% ~52s          
   |++++++++                                          | 15% ~51s          
   |++++++++                                          | 16% ~50s          
   |+++++++++                                         | 17% ~50s          
   |+++++++++                                         | 18% ~49s          
   |++++++++++                                        | 19% ~48s          
   |++++++++++                                        | 20% ~48s          
   |+++++++++++                                       | 21% ~47s          
   |+++++++++++                                       | 22% ~46s          
   |++++++++++++                                      | 23% ~45s          
   |++++++++++++                                      | 24% ~45s          
   |+++++++++++++                                     | 25% ~44s          
   |+++++++++++++                                     | 26% ~44s          
   |++++++++++++++                                    | 27% ~43s          
   |++++++++++++++                                    | 28% ~43s          
   |+++++++++++++++                                   | 29% ~42s          
   |+++++++++++++++                                   | 30% ~42s          
   |++++++++++++++++                                  | 31% ~41s          
   |++++++++++++++++                                  | 32% ~40s          
   |+++++++++++++++++                                 | 33% ~40s          
   |+++++++++++++++++                                | 34% ~39s          
   |++++++++++++++++++                                | 35% ~39s          
   |++++++++++++++++++                                | 36% ~38s          
   |+++++++++++++++++++                               | 37% ~37s          
   |+++++++++++++++++++                               | 38% ~37s          
   |++++++++++++++++++++                              | 39% ~36s          
   |++++++++++++++++++++                              | 40% ~35s          
   |+++++++++++++++++++++                             | 41% ~35s          
   |+++++++++++++++++++++                             | 42% ~34s          
   |++++++++++++++++++++++                            | 43% ~34s          
   |++++++++++++++++++++++                            | 44% ~33s          
   |+++++++++++++++++++++++                           | 45% ~32s          
   |+++++++++++++++++++++++                           | 46% ~32s          
   |++++++++++++++++++++++++                          | 47% ~31s          
   |++++++++++++++++++++++++                          | 48% ~31s          
   |+++++++++++++++++++++++++                         | 49% ~30s          
   |+++++++++++++++++++++++++                         | 50% ~29s          
   |++++++++++++++++++++++++++                        | 51% ~29s          
   |++++++++++++++++++++++++++                        | 52% ~28s          
   |+++++++++++++++++++++++++++                       | 53% ~28s          
   |+++++++++++++++++++++++++++                       | 54% ~27s          
   |++++++++++++++++++++++++++++                      | 55% ~26s          
   |++++++++++++++++++++++++++++                     | 56% ~26s          
   |+++++++++++++++++++++++++++++                     | 57% ~25s          
   |+++++++++++++++++++++++++++++                     | 58% ~25s          
   |++++++++++++++++++++++++++++++                    | 59% ~24s          
   |++++++++++++++++++++++++++++++                    | 60% ~23s          
   |+++++++++++++++++++++++++++++++                   | 61% ~23s          
   |+++++++++++++++++++++++++++++++                   | 62% ~22s          
   |++++++++++++++++++++++++++++++++                  | 63% ~22s          
   |++++++++++++++++++++++++++++++++                  | 64% ~21s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~21s          
   |+++++++++++++++++++++++++++++++++                 | 66% ~20s          
   |++++++++++++++++++++++++++++++++++                | 67% ~19s          
   |++++++++++++++++++++++++++++++++++               | 68% ~19s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~18s          
   |+++++++++++++++++++++++++++++++++++               | 70% ~18s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~17s          
   |++++++++++++++++++++++++++++++++++++              | 72% ~16s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~16s          
   |+++++++++++++++++++++++++++++++++++++             | 74% ~15s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~15s          
   |++++++++++++++++++++++++++++++++++++++            | 76% ~14s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~14s          
   |+++++++++++++++++++++++++++++++++++++++          | 78% ~13s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~12s          
   |++++++++++++++++++++++++++++++++++++++++         | 80% ~12s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~11s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~11s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~10s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~09s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~09s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~08s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~08s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~07s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~06s          
   |+++++++++++++++++++++++++++++++++++++++++++++    | 90% ~06s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~05s          
   |++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~05s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~04s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~04s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 58s

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~01m 50s      
   |++                                                | 2 % ~01m 49s      
   |++                                                | 3 % ~01m 48s      
   |+++                                               | 4 % ~01m 49s      
   |+++                                               | 5 % ~01m 51s      
   |++++                                              | 6 % ~01m 53s      
   |++++                                              | 7 % ~01m 55s      
   |+++++                                             | 8 % ~01m 52s      
   |+++++                                             | 9 % ~01m 50s      
   |++++++                                            | 10% ~01m 47s      
   |++++++                                            | 11% ~01m 47s      
   |+++++++                                           | 12% ~01m 45s      
   |+++++++                                           | 13% ~01m 43s      
   |++++++++                                          | 14% ~01m 42s      
   |++++++++                                          | 15% ~01m 40s      
   |+++++++++                                         | 16% ~01m 39s      
   |+++++++++                                         | 17% ~01m 38s      
   |++++++++++                                        | 18% ~01m 37s      
   |++++++++++                                        | 19% ~01m 35s      
   |+++++++++++                                       | 20% ~01m 34s      
   |+++++++++++                                       | 21% ~01m 32s      
   |++++++++++++                                      | 22% ~01m 32s      
   |++++++++++++                                      | 23% ~01m 30s      
   |+++++++++++++                                     | 24% ~01m 29s      
   |+++++++++++++                                     | 26% ~01m 28s      
   |++++++++++++++                                    | 27% ~01m 26s      
   |++++++++++++++                                    | 28% ~01m 25s      
   |+++++++++++++++                                   | 29% ~01m 24s      
   |+++++++++++++++                                   | 30% ~01m 23s      
   |++++++++++++++++                                  | 31% ~01m 22s      
   |++++++++++++++++                                  | 32% ~01m 20s      
   |+++++++++++++++++                                 | 33% ~01m 19s      
   |+++++++++++++++++                                 | 34% ~01m 18s      
   |++++++++++++++++++                                | 35% ~01m 17s      
   |++++++++++++++++++                                | 36% ~01m 15s      
   |+++++++++++++++++++                               | 37% ~01m 14s      
   |+++++++++++++++++++                               | 38% ~01m 13s      
   |++++++++++++++++++++                              | 39% ~01m 12s      
   |++++++++++++++++++++                              | 40% ~01m 11s      
   |+++++++++++++++++++++                             | 41% ~01m 09s      
   |+++++++++++++++++++++                             | 42% ~01m 08s      
   |++++++++++++++++++++++                            | 43% ~01m 07s      
   |++++++++++++++++++++++                            | 44% ~01m 06s      
   |+++++++++++++++++++++++                           | 45% ~01m 05s      
   |+++++++++++++++++++++++                           | 46% ~01m 04s      
   |++++++++++++++++++++++++                          | 47% ~01m 03s      
   |++++++++++++++++++++++++                          | 48% ~01m 01s      
   |+++++++++++++++++++++++++                         | 49% ~60s          
   |+++++++++++++++++++++++++                         | 50% ~59s          
   |++++++++++++++++++++++++++                        | 51% ~57s          
   |+++++++++++++++++++++++++++                       | 52% ~56s          
   |+++++++++++++++++++++++++++                       | 53% ~55s          
   |++++++++++++++++++++++++++++                      | 54% ~54s          
   |++++++++++++++++++++++++++++                      | 55% ~52s          
   |+++++++++++++++++++++++++++++                     | 56% ~51s          
   |+++++++++++++++++++++++++++++                     | 57% ~50s          
   |++++++++++++++++++++++++++++++                    | 58% ~49s          
   |++++++++++++++++++++++++++++++                    | 59% ~48s          
   |+++++++++++++++++++++++++++++++                   | 60% ~46s          
   |+++++++++++++++++++++++++++++++                   | 61% ~45s          
   |++++++++++++++++++++++++++++++++                  | 62% ~44s          
   |++++++++++++++++++++++++++++++++                  | 63% ~43s          
   |+++++++++++++++++++++++++++++++++                 | 64% ~42s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~40s          
   |++++++++++++++++++++++++++++++++++                | 66% ~39s          
   |++++++++++++++++++++++++++++++++++                | 67% ~38s          
   |+++++++++++++++++++++++++++++++++++               | 68% ~37s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~36s          
   |++++++++++++++++++++++++++++++++++++              | 70% ~34s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~33s          
   |+++++++++++++++++++++++++++++++++++++             | 72% ~32s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~31s          
   |++++++++++++++++++++++++++++++++++++++            | 74% ~29s          
   |++++++++++++++++++++++++++++++++++++++            | 76% ~28s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~27s          
   |+++++++++++++++++++++++++++++++++++++++           | 78% ~26s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~25s          
   |++++++++++++++++++++++++++++++++++++++++          | 80% ~24s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~22s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~21s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~20s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~19s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~18s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~17s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~15s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~14s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~13s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~12s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~11s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~10s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~08s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~07s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~06s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~05s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~04s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 56s

Display the top markers you computed above.

tiss.markers %>% group_by(cluster)

Assigning cell type identity to clusters

At a coarse level, we can use canonical markers to match the unbiased clustering to known cell types:

0: Basal/differentiating cells 1: Basal/differentiating cells 2: Proliferating cells 3: Maturing/nonkeratinizing cells 4: Basal/differentiating cells 5: Stratified/differentiated keratinocytes 6: Basal/differentiating cells 7: Filliform Keratinocytes

# stash current cluster IDs
tiss <- StashIdent(object = tiss, save.name = "cluster.ids")

# enumerate current cluster IDs and the labels for them
cluster.ids <- c(0, 1, 2, 3, 4, 5, 6, 7)
cell_ontology_class <- c("basal cell of epidermis", "basal cell of epidermis", "keratinocyte", "keratinocyte", "basal cell of epidermis", "keratinocyte", "basal cell of epidermis", "keratinocyte" )
cell_ontology_id <- c("CL:0000312", "CL:0000312", "CL:0000312", "CL:0000312", "CL:0000312", "CL:0000312", "CL:0000312", "CL:0000312" )

tiss@meta.data[,'cell_ontology_class'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_class)
tiss@meta.data[,'cell_ontology_id'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_id)

tiss@meta.data[tiss@cell.names,'cell_ontology_class'] <- as.character(tiss@meta.data$cell_ontology_class)
tiss@meta.data[tiss@cell.names,'cell_ontology_id'] <- as.character(tiss@meta.data$cell_ontology_id)

TSNEPlot(object = tiss, do.label = TRUE, pt.size = 1, label.size = 3, group.by='cell_ontology_class', do.return = TRUE)
#dev.copy(png,'annotated_plot-2.png')
#dev.off()

Checking for batch effects

Color by metadata, like plate barcode, to check for batch effects.

TSNEPlot(object = tiss, do.return = TRUE, group.by = "mouse.sex")

Print a table showing the count of cells in each identity category from each plate.

table(as.character(tiss@ident), as.character(tiss@meta.data$plate.barcode))

Save the Robject for later

When you save the annotated tissue, please give it a name.

filename = here('00_data_ingest', '04_tissue_robj_generated', 
                     paste0("facs", tissue_of_interest, "_seurat_tiss.Robj"))
print(filename)
save(tiss, file=filename)
# To reload a saved object
filename = here('00_data_ingest', '04_tissue_robj_generated', 
                      paste0("facs", tissue_of_interest, "_seurat_tiss.Robj"))
load(file=filename)

Export the final metadata

So that Biohub can easily combine all your cell_ontology_classs, please export them as a simple csv.

head(tiss@meta.data)
filename = here('00_data_ingest', '03_tissue_cell_ontology_class_csv', 
                     paste0(tissue_of_interest, "_cell_ontology_class.csv"))
write.csv(tiss@meta.data[,c('plate.barcode','cell_ontology_class','cell_ontology_id')], file=filename)
---
title: "Tongue FACS Notebook"
output: html_notebook
---

Enter the directory of the maca folder on your drive and the name of the tissue you want to analyze.

```{r}
tissue_of_interest = "Tongue"
```

Load the requisite packages and some additional helper functions.

```{r}
library(here)
library(useful)
library(Seurat)
library(dplyr)
library(Matrix)
library(ontologyIndex)
cell_ontology = get_ontology('https://raw.githubusercontent.com/obophenotype/cell-ontology/master/cl-basic.obo', extract_tags='everything')

validate_cell_ontology = function(cell_ontology_class){
  in_cell_ontology = sapply(cell_ontology_class, function(x) is.element(x, cell_ontology$name))
  if (!all(in_cell_ontology)) {
    message = paste0('"', cell_ontology_class[!in_cell_ontology], '" is not in the cell ontology
')
    stop(message)
  }
}
convert_to_cell_ontology_id = function(cell_ontology_class){
  return(sapply(cell_ontology_class, function(x) as.vector(cell_ontology$id[cell_ontology$name == x])[1]))
}

save_dir = here('00_data_ingest', 'tissue_robj')
```



```{r}
# read the metadata to get the plates we want
plate_metadata_filename = here('00_data_ingest', '00_facs_raw_data', 'metadata_FACS.csv')

plate_metadata <- read.csv(plate_metadata_filename, sep=",", header = TRUE)
colnames(plate_metadata)[1] <- "plate.barcode"
plate_metadata
```

Subset the metadata on the tissue.

```{r}
tissue_plates = filter(plate_metadata, tissue == tissue_of_interest)[,c('plate.barcode','tissue','subtissue','mouse.sex')]
tissue_plates
```

Load the read count data.
```{r}
#Load the gene names and set the metadata columns by opening the first file
filename = here('00_data_ingest', '00_facs_raw_data', 'FACS', paste0(tissue_of_interest, '-counts.csv'))

raw.data = read.csv(filename, sep=",", row.names=1)
# raw.data = data.frame(row.names = rownames(raw.data))
corner(raw.data)
```
Make a vector of plate barcodes for each cell

```{r}
plate.barcodes = lapply(colnames(raw.data), function(x) strsplit(strsplit(x, "_")[[1]][1], '.', fixed=TRUE)[[1]][2])
head(plate.barcodes)
```

Use only the metadata rows corresponding to Bladder plates. Make a plate barcode dataframe to "expand" the per-plate metadata to be per-cell.
```{r}
barcode.df = t.data.frame(as.data.frame(plate.barcodes))

rownames(barcode.df) = colnames(raw.data)
colnames(barcode.df) = c('plate.barcode')
head(barcode.df)

rnames = row.names(barcode.df)
meta.data <- merge(barcode.df, plate_metadata, by='plate.barcode', sort = F)
row.names(meta.data) <- rnames

# Sort cells by plate barcode because that's how the data was originally
meta.data = meta.data[order(meta.data$plate.barcode), ]
corner(meta.data)
raw.data = raw.data[, rownames(meta.data)]
corner(raw.data)
```
Process the raw data and load it into the Seurat object.

```{r}
# Find ERCC's, compute the percent ERCC, and drop them from the raw data.
erccs <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = TRUE)
percent.ercc <- Matrix::colSums(raw.data[erccs, ])/Matrix::colSums(raw.data)
ercc.index <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = FALSE)
raw.data <- raw.data[-ercc.index,]

# Create the Seurat object with all the data
tiss <- CreateSeuratObject(raw.data = raw.data, project = tissue_of_interest, 
                    min.cells = 5, min.genes = 5)

tiss <- AddMetaData(object = tiss, meta.data)
tiss <- AddMetaData(object = tiss, percent.ercc, col.name = "percent.ercc")
# Change default name for sums of counts from nUMI to nReads
colnames(tiss@meta.data)[colnames(tiss@meta.data) == 'nUMI'] <- 'nReads'

# Create metadata columns for cell_ontology_classs and subcell_ontology_classs
tiss@meta.data[,'cell_ontology_class'] <- NA
tiss@meta.data[,'subcell_ontology_class'] <- NA
```


Calculate percent ribosomal genes.

```{r}
ribo.genes <- grep(pattern = "^Rp[sl][[:digit:]]", x = rownames(x = tiss@data), value = TRUE)
percent.ribo <- Matrix::colSums(tiss@raw.data[ribo.genes, ])/Matrix::colSums(tiss@raw.data)
tiss <- AddMetaData(object = tiss, metadata = percent.ribo, col.name = "percent.ribo")
```

A sanity check: genes per cell vs reads per cell.

```{r}
GenePlot(object = tiss, gene1 = "nReads", gene2 = "nGene", use.raw=T)
```

Filter out cells with few reads and few genes.

```{r}
tiss <- FilterCells(object = tiss, subset.names = c("nGene", "nReads"), 
    low.thresholds = c(500, 50000), high.thresholds = c(25000, 2000000))
```


Normalize the data, then regress out correlation with total reads
```{r}
tiss <- NormalizeData(object = tiss, scale.factor = 1e6)
tiss <- ScaleData(object = tiss)
tiss <- FindVariableGenes(object = tiss, do.plot = TRUE, x.high.cutoff = Inf, y.cutoff = 0.5)
```


Run Principal Component Analysis.
```{r}
tiss <- RunPCA(object = tiss, do.print = FALSE)
tiss <- ProjectPCA(object = tiss, do.print = FALSE)
```

```{r, echo=FALSE, fig.height=4, fig.width=8}
PCHeatmap(object = tiss, pc.use = 1:3, cells.use = 1000, do.balanced = TRUE, label.columns = FALSE, num.genes = 8)
```

Later on (in FindClusters and TSNE) you will pick a number of principal components to use. This has the effect of keeping the major directions of variation in the data and, ideally, supressing noise. There is no correct answer to the number to use, but a decent rule of thumb is to go until the plot plateaus.

```{r}
PCElbowPlot(object = tiss)
```

Choose the number of principal components to use.
```{r}
# Set number of principal components. 
n.pcs = 10
```


The clustering is performed based on a nearest neighbors graph. Cells that have similar expression will be joined together. The Louvain algorithm looks for groups of cells with high modularity--more connections within the group than between groups. The resolution parameter determines the scale...higher resolution will give more clusters, lower resolution will give fewer.

For the top-level clustering, aim to under-cluster instead of over-cluster. It will be easy to subset groups and further analyze them below.

```{r}
# Set resolution 
res.used <- 0.5

tiss <- FindClusters(object = tiss, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
```

To visualize 
```{r}
# If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10).
tiss <- RunTSNE(object = tiss, dims.use = 1:n.pcs, seed.use = 10, perplexity=30)
# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = tiss, do.label = T, pt.size = 1.2, label.size = 4)

```


Check expression of genes of interset.

```{r, echo=FALSE, fig.height=12, fig.width=8}
genes_to_check = c('Top2a', 'Cdc20', 'Mki67', 'Krt5', 'Krt14', 'Krt15', 'Krt84', 'Krt36', 'Hoxc13', 'Krt4', 'Krt13', 'Krt78', 'Krt10', 'Sbsn', 'Dcn')

FeaturePlot(tiss, genes_to_check, pt.size = 1.5, nCol = 3)
```

Dotplots let you see the intensity of exppression and the fraction of cells expressing for each of your genes of interest.

```{r, echo=FALSE, fig.height=4, fig.width=8}
# To change the y-axis to show raw counts, add use.raw = T.
DotPlot(tiss, genes_to_check, plot.legend = T)
```

How big are the clusters?
```{r}
table(tiss@ident)
```

```{r}
tiss1=BuildClusterTree(tiss)
```

```{r}
marker10=FindAllMarkersNode(tiss1, 10)
```
```{r}
print(x = head(x= marker10, n = 30))
```



Which markers identify a specific cluster?

```{r}
clust.markers <- FindMarkers(object = tiss, ident.1 = 2, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
```

```{r}
print(x = head(x= clust.markers, n = 30))
```

You can also compute all markers for all clusters at once. This may take some time.
```{r}
tiss.markers <- FindAllMarkers(object = tiss, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
```

Display the top markers you computed above.
```{r}
tiss.markers %>% group_by(cluster)
```

## Assigning cell type identity to clusters

At a coarse level, we can use canonical markers to match the unbiased clustering to known cell types:

0: Basal/differentiating cells
1: Basal/differentiating cells
2: Proliferating cells
3: Maturing/nonkeratinizing cells
4: Basal/differentiating cells
5: Stratified/differentiated keratinocytes
6: Basal/differentiating cells
7: Filliform Keratinocytes

```{r}
# stash current cluster IDs
tiss <- StashIdent(object = tiss, save.name = "cluster.ids")

# enumerate current cluster IDs and the labels for them
cluster.ids <- c(0, 1, 2, 3, 4, 5, 6, 7)
cell_ontology_class <- c("basal cell of epidermis", "basal cell of epidermis", "keratinocyte", "keratinocyte", "basal cell of epidermis", "keratinocyte", "basal cell of epidermis", "keratinocyte" )
cell_ontology_id <- c("CL:0000312", "CL:0000312", "CL:0000312", "CL:0000312", "CL:0000312", "CL:0000312", "CL:0000312", "CL:0000312" )

tiss@meta.data[,'cell_ontology_class'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_class)
tiss@meta.data[,'cell_ontology_id'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_id)

tiss@meta.data[tiss@cell.names,'cell_ontology_class'] <- as.character(tiss@meta.data$cell_ontology_class)
tiss@meta.data[tiss@cell.names,'cell_ontology_id'] <- as.character(tiss@meta.data$cell_ontology_id)

TSNEPlot(object = tiss, do.label = TRUE, pt.size = 1, label.size = 3, group.by='cell_ontology_class', do.return = TRUE)
#dev.copy(png,'annotated_plot-2.png')
#dev.off()
```


## Checking for batch effects


Color by metadata, like plate barcode, to check for batch effects.
```{r}
TSNEPlot(object = tiss, do.return = TRUE, group.by = "mouse.sex")
```

Print a table showing the count of cells in each identity category from each plate.

```{r}
table(as.character(tiss@ident), as.character(tiss@meta.data$plate.barcode))
```



# Save the Robject for later
When you save the annotated tissue, please give it a name.

```{r}
filename = here('00_data_ingest', '04_tissue_robj_generated', 
                     paste0("facs", tissue_of_interest, "_seurat_tiss.Robj"))
print(filename)
save(tiss, file=filename)
```

```{r}
# To reload a saved object
filename = here('00_data_ingest', '04_tissue_robj_generated', 
                      paste0("facs", tissue_of_interest, "_seurat_tiss.Robj"))
load(file=filename)
```



# Export the final metadata

So that Biohub can easily combine all your cell_ontology_classs, please export them as a simple csv.

```{r}
head(tiss@meta.data)
```


```{r}
filename = here('00_data_ingest', '03_tissue_cell_ontology_class_csv', 
                     paste0(tissue_of_interest, "_cell_ontology_class.csv"))
write.csv(tiss@meta.data[,c('plate.barcode','cell_ontology_class','cell_ontology_id')], file=filename)
```


